code: def scatter_nd_sub(
      ref, indices, updates, use_locking=False, name=None
  ):
      r"""Applies sparse subtraction to individual values or slices in a Variable.
  
      `ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.
  
      `indices` must be integer tensor, containing indices into `ref`.
      It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.
  
      The innermost dimension of `indices` (with length `K`) corresponds to
      indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th
      dimension of `ref`.
  
      `updates` is `Tensor` of rank `Q-1+P-K` with shape:
  
      ```
      [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]]
      ```
  
      For example, say we want to subtract 4 scattered elements from a rank-1 tensor
      with 8 elements. In Python, that update would look like this:
  
      ```python
      ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
      indices = tf.constant([[4], [3], [1] ,[7]])
      updates = tf.constant([9, 10, 11, 12])
      op = tf.compat.v1.scatter_nd_sub(ref, indices, updates)
      with tf.compat.v1.Session() as sess:
        print sess.run(op)
      ```
  
      The resulting update to ref would look like this:
  
          [1, -9, 3, -6, -6, 6, 7, -4]
  
      See `tf.scatter_nd` for more details about how to make updates to
      slices.
  
      Args:
        ref: A mutable `Tensor`. Must be one of the following types: `float32`,
          `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`,
          `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`,
          `uint32`, `uint64`. A mutable Tensor. Should be from a Variable node.
        indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.
          A tensor of indices into ref.
        updates: A `Tensor`. Must have the same type as `ref`.
          A tensor of updated values to add to ref.
        use_locking: An optional `bool`. Defaults to `False`.
          An optional bool. Defaults to True. If True, the assignment will
          be protected by a lock; otherwise the behavior is undefined,
          but may exhibit less contention.
        name: A name for the operation (optional).
  
      Returns:
        A mutable `Tensor`. Has the same type as `ref`.
      """
      if ref.dtype._is_ref_dtype:
          return gen_state_ops.scatter_nd_sub(
              ref, indices, updates, use_locking, name
          )
      return ref._lazy_read(
          gen_state_ops.resource_scatter_nd_sub(  # pylint: disable=protected-access
              ref.handle,
              indices,
              ops.convert_to_tensor(updates, ref.dtype),
              name=name,
          )
      )
  
imports: from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gen_math_ops
  from tensorflow.python.ops import gen_resource_variable_ops
  from tensorflow.python.ops import gen_state_ops
  from tensorflow.python.ops import state_grad
  from tensorflow.python.ops.gen_state_ops import *
  from tensorflow.python.util import deprecation
  from tensorflow.python.util.deprecation import deprecated
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 14 for calling a global function with example usage: scatter_nd_sub(ref, indices, updates, use_locking, name) and returns: ref, gen_state_ops